CN113378457A - Knowledge modeling method and system for intelligent calculation and adjustment of power grid load flow - Google Patents
Knowledge modeling method and system for intelligent calculation and adjustment of power grid load flowDownload PDFInfo
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Abstract
Translated fromChinese
Description
Technical Field
The application relates to the technical field of power systems, in particular to a knowledge modeling method and a knowledge modeling system for intelligent calculation and adjustment of power grid load flow.
Background
With the development of domestic electric power systems, especially the comprehensive development of smart power grids and extra-high voltage engineering construction, the power grid structure is increasingly complex, the number of power grid elements is gradually increased, and the power grid data is increased explosively. The amount of data in the system is increased sharply, and if the excessive data cannot be processed in time, decision delay, errors and even power accidents can be caused. Therefore, massive power grid data need to be extracted, the extracted information needs to be further abstracted and promoted to knowledge level research, a power grid knowledge representation, storage and synergy are analyzed, and a power grid knowledge base is constructed on the basis, so that the related calculation, analysis, decision and management system of a power grid can be more intelligent, and related workers of the power grid can obtain needed knowledge and information more quickly.
The power flow calculation is the most basic and important calculation for researching the power system and is the basis for planning and operating the power grid. With the continuous expansion of the power grid scale, the variables of the power flow equation are also increased sharply, and meanwhile, various constraint conditions such as voltage, power and the like are also required to be met, and the power flow calculation is often in a non-convergence condition. At this time, the operation mode needs to be adjusted by means of changing the output of the generator, switching the capacitance reactor and the like, so that the power flow returns to a reasonable feasible solution, and the reasonable distribution of the power flow is ensured. The process of load flow calculation and adjustment usually depends on manual experience accumulated by mode calculation personnel for a long time, the adjustment precision is not guaranteed, the phenomenon of error and leakage is easily caused, and the efficiency is very low. Therefore, it is necessary to summarize the knowledge and experience in the power flow calculation and adjustment process, and express the knowledge in a certain expression manner to guide and assist the manner calculator to make more accurate judgment on the adjustment direction and the adjustment means, thereby effectively reducing the burden of the manner calculator.
In the aspect of knowledge expression, the current power grid simulation knowledge mainly adopts three forms based on relationship, XML and ontology language. The relationship-based simulation knowledge base construction is the earliest and most easily understood method, and a relational database is constructed according to the relationship among units (buses, transformers, generators, switches, disconnecting links and the like) in the power grid calculation data. The construction of the XML-based grid simulation knowledge base is derived from the storage requirements of event data in the power system. However, the power grid domain knowledge expression based on the two methods has the disadvantages of obscure knowledge representation, low reasoning efficiency, poor visualization level, difficulty in dynamic knowledge cooperative processing and the like. With the development of ontology language OWL, knowledge graph becomes the mainstream form of knowledge representation. Knowledge-graph represents entities and their associations in a graph structure, where entities are represented as points in the graph and associations are represented as edges in the graph. Researchers gradually adopt ontology language form to express power grid simulation knowledge.
Although knowledge representation forms of ontology languages and graph structures are widely accepted, many problems still exist in the aspects of computing efficiency, data sparsity and the like, and need to be solved. In the aspect of power grid simulation, a modeling mode aiming at a complex knowledge structure is still lacked; and because qualitative knowledge, quantitative knowledge, correlation knowledge and affair knowledge exist in the power grid simulation at the same time, the existing ontology and knowledge map model are not enough to effectively express the knowledge.
Disclosure of Invention
The embodiment of the disclosure provides a knowledge modeling method and a knowledge modeling system for intelligent calculation and adjustment of power grid load flow, which at least solve the problem that in the prior art, knowledge representation of a body language and a graph structure still lacks a modeling mode aiming at a complex knowledge structure in the aspect of power grid simulation; and because qualitative knowledge, quantitative knowledge, correlation knowledge and affair knowledge exist in the power grid simulation at the same time, the existing ontology and knowledge map model are not enough to effectively express the knowledge.
According to an aspect of the disclosed embodiments, there is provided a knowledge modeling method for intelligent calculation and adjustment of power flow of a power grid, including: dividing knowledge involved in intelligent calculation and adjustment of the power grid load flow into power grid data knowledge, power grid state knowledge and adjustment rule knowledge; expressing the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in a triple form, and constructing a knowledge base; and according to the current known fact, reasoning is carried out according to a reasoning method and a control strategy by utilizing the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in the knowledge base, so that the knowledge base is updated and perfected, and the correctness and the integrity of the knowledge are ensured.
According to another aspect of the embodiments of the present disclosure, there is also provided a knowledge modeling system for intelligent calculation and adjustment of power flow of a power grid, including: the knowledge dividing module is used for dividing knowledge involved in intelligent calculation and adjustment of the power grid load flow into power grid data knowledge, power grid state knowledge and adjustment rule knowledge; the knowledge base building module is used for representing the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in a triple form to build a knowledge base; and the reasoning module is used for reasoning according to a reasoning method and a control strategy by using the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in the knowledge base according to the currently known fact, updating and perfecting the knowledge base and ensuring the correctness and the integrity of the knowledge.
In the invention, the grid data knowledge, the grid state knowledge and the adjustment rule knowledge are expressed in the form of < node, relation, node > triple, different node types and relations are defined according to different knowledge, and complex adjustment means in the grid adjustment process are packaged into independent function functions, so that the relations between grid elements and parameters and between elements can be accurately described, the current state of the grid can be effectively represented, and corresponding adjustment operation can be flexibly performed according to different states.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a schematic flow chart of a knowledge modeling method for intelligent calculation and adjustment of power flow in a power grid according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram illustrating a part of knowledge representation of intelligent calculation and adjustment of power flow in a power grid according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a power flow intelligent calculation and adjustment part knowledge graph according to an embodiment of the disclosure;
fig. 4 is a schematic diagram illustrating an example adjustment result of a CEPRI 36 node according to an embodiment of the disclosure;
fig. 5 is a schematic flow diagram of a knowledge modeling system for intelligent calculation and adjustment of power flow in a power grid according to an embodiment of the present disclosure.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
According to a first aspect of the present embodiment, a knowledge modeling method 100 for intelligent calculation and adjustment of grid load flow is provided. Referring to fig. 1, the method 100 includes:
s102, dividing knowledge involved in intelligent calculation and adjustment of the power grid load flow into power grid data knowledge, power grid state knowledge and adjustment rule knowledge;
s104, expressing the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in a triple form, and constructing a knowledge base;
and S106, reasoning according to a reasoning method and a control strategy by using the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in the knowledge base according to the currently known fact, updating and perfecting the knowledge base, and ensuring the correctness and the integrity of the knowledge.
Specifically, the knowledge modeling method for intelligent calculation and adjustment of the power grid load flow comprises the following steps: the method comprises three parts of power grid load flow calculation and adjustment knowledge classification and extraction, knowledge representation and knowledge reasoning. The power grid load flow calculation, the adjustment knowledge classification and the extraction are the basis of knowledge modeling, the knowledge representation is the core content of the knowledge modeling, and the knowledge reasoning is an important means for ensuring the correctness and the integrity of the knowledge.
The knowledge representation is a description of knowledge and is a data structure which is acceptable for a computer and is used for describing the knowledge, and the representation of the knowledge is that the knowledge is represented into a certain data structure which is convenient for the computer to store and utilize. The method adopts a form of a triple group to represent the power grid load flow calculation and adjustment knowledge.
Referring to fig. 2 and 3, a triple is composed of < node, relationship, node >. A node is described by a string of characters called the name of the node. The node comprises: the system comprises state nodes, characteristic nodes, operation nodes, power grid element nodes, power grid parameter nodes, position nodes, function nodes, weight nodes and node type description nodes.
The name of the state node describes a state, such as a non-convergence state, a trend out-of-limit state and the like. The state nodes must have the relationship of "featured", "operated" and their corresponding subsequent nodes to form a triple.
The name of the characteristic node describes a characteristic which is extracted from the power grid data and used for judging which state node the power grid is in. The description of the characteristic is an expression capable of judging True or False, and if the expression is True, the knowledge base can intelligently judge that the power grid conforms to the characteristic according to the expression.
The operation node, whose name describes an operation, is the tail node of the relationship "operation". After the knowledge base finds the corresponding state according to the features, the operation corresponding to the state is found and executed. Because the specific operations related to the power grid load flow calculation and adjustment are complex, various operations are packaged into corresponding function functions, and the knowledge base selects the corresponding functions to call according to the triples. When the operation is executed in this way, the operation node has two relationships and corresponding tail nodes: 1) the relationship "yes" and the tail node is "function call". Indicating that the operation will specify a function to call. 2) The relation "call", the tail node is "function node", the function to be called for the operation is specified.
The grid element node is used for describing a grid element entity, such as a generator, a transformer and the like. The relation "has attributes" can be used to connect the parameters used by the element in the process of power flow calculation and adjustment, such as "generator active power", "transformer transformation ratio", and the like.
And the power grid parameter node is used for describing parameters used in the power grid load flow calculation and adjustment process. The relation "location" is needed to connect "location nodes" to find the location of the file where the parameter is located.
The position node is used for describing the position of a parameter which can be used in the power grid adjustment, and a specific value corresponding to the parameter can be obtained from the position.
The function node represents a function, the name of the function node is the name of the function, and the triple containing the function node needs to perform necessary supplementary description on the parameter and the return value type of the function.
The weight node, which is the end node of the relationship "weight", has a name of one number, defaulted to 1, and is used to define the priority of each operation or state.
The node type describes a node, which is a tail node of the relationship "yes", describing the type of a node. Such as < (state node), "yes", "state" >, < (operation node), "yes", and "function call" >.
The relation is a name which connects two nodes in a knowledge base and represents the direct logical relation of the two nodes. Can be seen as a directional arrow, through a relationship, one node can find another node. The relationship includes: there are features, operations, yes, call, there is an attribute, there is a location, weight.
The characteristic is that the head node is a state node, the tail node is a characteristic node, which indicates what kind of characteristic a state has, and the knowledge base judges what kind of state the power grid is in by checking whether the power grid data conforms to the characteristic.
In the operation, the head node is a state node, and the tail node is an operation node, which indicates which operations need to be executed in one state. The knowledge base can sequentially execute each operation according to the weight after checking the state.
The head node is any node, and the tail node is a node type description node and represents the type of a node.
In the calling, the head node is an operation node, the tail node is a function node, which indicates which function is called by one operation, and the function node indicates the called function.
The attributes are the parameters which are generally used in the load flow calculation and adjustment process and can find the position, the head node is a power grid element node, and the tail node is a power grid parameter node and represents the attributes of the power grid element.
The knowledge base can find the parameter in the file through the position relation and execute corresponding operation.
The weight relationship can give weight to the operation or the state if a plurality of operations or a plurality of states exist, and defines the priority of execution or search.
The knowledge reasoning mechanism utilizes knowledge in a knowledge base to carry out reasoning according to a certain reasoning method and a certain control strategy according to the current known fact, and obtains the answer of the problem or proves the correctness of a certain hypothesis.
The reasoning method comprises deductive reasoning, inductive reasoning, uncertain reasoning, non-monotonic reasoning, qualitative reasoning and the like.
The deduction reasoning is to deduce new conclusions based on the fact that actual problems are newly added, the conclusions are kept to be inconsistent with the existing knowledge and conclusions, and the deduction reasoning is to deduce the fact that a problem is included in known facts according to an axiomatic system.
The control strategy of the reasoning process mainly solves the knowledge selection and application sequence of the whole problem solving process. There are three general control strategies for inference processes: the invention adopts a forward reasoning strategy, a reverse reasoning strategy and a forward and reverse mixed reasoning strategy.
The forward and reverse mixed reasoning control strategy comprises the following specific steps: firstly, a batch of targets are generated according to part of problem information provided by a user, then, further information is obtained for each generated target, and the targets are tested one by one. The core of this is the early elimination of solutions that are inconsistent with current problem data constraints.
Verification is carried out through a CEPRI 36 node example, the method of the embodiment is applied to the example, and intelligent load flow calculation and adjustment are carried out on a sample which is not converged according to grid knowledge expressed by a triple group and calling of a corresponding operation function, so that calculation convergence is achieved. The test results prove the effectiveness of the invention.
Further, taking 1 group of data as an example, the initial power flow of the data is not converged, that is, the power flow is calculated on the data, and the convergence flag is 1. According to knowledge < unconverged state, characterized, convergence flag is 1> and < unconverged state, subsequent state, check parameter state >, judge the data is unconverged state, and enter parameter check state. According to the knowledge < checking parameter state, operation, checking transformer transformation ratio >, starting to check whether the transformer transformation ratio filling in the data is in a reasonable range. According to the knowledge, the functions are called >, < checking the transformer transformation ratio, called, r ═ checkTransTk (upper limit of the transformer transformation ratio, lower limit of the transformer transformation ratio, log structure of the transformer transformation ratio adjustment, number of transformer transformation ratio adjustments) >, < upper limit of the transformer transformation ratio, parameter value, 1.3>, < lower limit of the transformer transformation ratio, parameter value, 0.7>, and the transformer transformation ratios greater than 1.3 and less than 0.7 in the data are adjusted to the default value of 1.0 by calling the checking transformer transformation ratio function checkTransTk. The adjustment results are shown with reference to fig. 4.
Therefore, the grid data knowledge, the grid state knowledge and the adjustment rule knowledge are expressed in the form of the < node, relation and node > triple, different node types and relations are defined according to different knowledge, and complex adjustment means in the grid adjustment process are packaged into independent function functions, so that the relations between grid elements and parameters and between elements can be accurately described, the current state of the grid can be effectively represented, and corresponding adjustment operation can be flexibly performed according to different states.
Optionally, the grid data knowledge refers to knowledge related to grid component elements and attributes, parameters and characteristics thereof; the power grid state knowledge refers to knowledge related to the power grid state, and comprises a normal state, a non-convergence state, a section adjustment state and a load flow out-of-limit state; the regulation rule knowledge refers to knowledge related to the adopted regulation measures and the applied regulation rules when the power grid is transited from one state to another state for regulation.
Optionally, the triplet consists of < node, relationship, node >; said node is a string, said string being called a name node of said node, said node comprising: the system comprises state nodes, characteristic nodes, operation nodes, power grid element nodes, power grid parameter nodes, position nodes, function nodes, weight nodes and node type description nodes; the relationship is used for connecting two nodes and represents the name of a direct logical relationship between the two nodes, and the relationship comprises: there are features, operations, yes, call, there is an attribute, there is a location, weight.
Optionally, according to currently known facts, reasoning is performed according to a reasoning method and a control strategy by using the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in the knowledge base, and an answer to the question or correctness of a certain hypothesis is obtained or proved, including: new conclusions are drawn from the fact that the actual problem is newly added, and from the fact that the axiomatic system includes a problem in the known fact, the new conclusions are drawn as conclusions, which remain in contradiction to the existing knowledge and conclusions.
Optionally, according to the currently known fact, the method performs inference according to an inference method and a control strategy by using the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in the knowledge base to obtain an answer to the question or prove the correctness of a certain hypothesis, and further includes: generating a batch of targets according to part of problem information provided by a user; and acquiring further information of each generated target, testing one by one, and removing a solution inconsistent with the constraint of the current problem data.
Therefore, the grid data knowledge, the grid state knowledge and the adjustment rule knowledge are expressed in the form of the < node, relation and node > triple, different node types and relations are defined according to different knowledge, and complex adjustment means in the grid adjustment process are packaged into independent function functions, so that the relations between grid elements and parameters and between elements can be accurately described, the current state of the grid can be effectively represented, and corresponding adjustment operation can be flexibly performed according to different states.
According to another aspect of the present embodiment, aknowledge modeling system 500 for intelligent calculation and adjustment of grid load flow is also provided. Referring to fig. 5, thesystem 500 includes: a dividing knowledge module 510, configured to divide knowledge involved in power grid load flow intelligent calculation and adjustment into power grid data knowledge, power grid state knowledge, and adjustment rule knowledge; a knowledge base building module 520, configured to represent the power grid data knowledge, the power grid state knowledge, and the adjustment rule knowledge in a triple form, and build a knowledge base; and the inference module 530 is used for performing inference according to an inference method and a control strategy by using the power grid data knowledge, the power grid state knowledge and the adjustment rule knowledge in the knowledge base according to the currently known fact, and updating and perfecting the knowledge base to ensure the correctness and the integrity of the knowledge.
Optionally, the grid data knowledge refers to knowledge related to grid component elements and attributes, parameters and characteristics thereof; the power grid state knowledge refers to knowledge related to the power grid state, and comprises a normal state, a non-convergence state, a section adjustment state and a load flow out-of-limit state; the regulation rule knowledge refers to knowledge related to the adopted regulation measures and the applied regulation rules when the power grid is transited from one state to another state for regulation.
Optionally, the triplet consists of < node, relationship, node >; said node is a string, said string being called a name node of said node, said node comprising: the system comprises state nodes, characteristic nodes, operation nodes, power grid element nodes, power grid parameter nodes, position nodes, function nodes, weight nodes and node type description nodes; the relationship is used for connecting two nodes and represents the name of a direct logical relationship between the two nodes, and the relationship comprises: there are features, operations, yes, call, there is an attribute, there is a location, weight.
Optionally, the inference module 530, comprising: and the derivation submodule is used for deriving a new conclusion according to the fact that the actual problem is newly added, and deriving a conclusion according to the fact that the axiom system includes the known fact in the problem, wherein the new conclusion keeps the existing knowledge and conclusion without contradiction.
Optionally, the inference module 530 further comprises: the generation target submodule is used for generating a batch of targets according to part of problem information provided by a user; and the test elimination submodule is used for acquiring the further information of each generated target, testing one by one and eliminating the solution inconsistent with the constraint of the current problem data.
Theknowledge modeling system 500 for intelligent calculation and adjustment of power grid load flow according to the embodiment of the present invention corresponds to the knowledge modeling method 100 for intelligent calculation and adjustment of power grid load flow according to another embodiment of the present invention, and is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
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